Aerosol optical depth retrieval over land from two angle view satellite radiometry

Atmospheric aerosol particles play an important role in the Earth’s radiation balance. They are considered one of the largest uncertainties in today’s climate modelling. To a large extent, these uncertainties are caused by the lack of aerosol data on a global scale. Due to the short lifetimes of aerosols in the troposphere (hours to a week), and the many different sources with different spatial extents and emissions, the aerosol is highly variable in both space and time. Satellite remote sensing only can provide the global coverage and the spatial and temporal resolution to measure the inhomogeneous aerosol fields

The Ozone Monitoring Instrument: Overview of 14 years in space

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain

Daily Satellite Observations of Methane from Oil and Gas Production Regions in the United States

Production of oil and natural gas in North America is at an all-time high due to the development and use of horizontal drilling and hydraulic fracturing. Methane emissions associated with this industrial activity are a concern because of the contribution to climate radiative forcing. We present new measurements from the space-based TROPOspheric Monitoring Instrument (TROPOMI) launched in 2017 that show methane enhancements over production regions in the United States. In the Uintah Basin in Utah, TROPOMI methane columns correlated with in-situ measurements, and the highest columns were observed over the deepest parts of the basin, consistent with the accumulation of emissions underneath inversions. In the Permian Basin in Texas and New Mexico, methane columns showed maxima over regions with the highest natural gas production and were correlated with nitrogen-dioxide columns at a ratio that is consistent with results from in-situ airborne measurements. The improved detail provided by TROPOMI will likely enable the timely monitoring from space of methane emissions associated with oil and natural gas production.</p

Validation of the TROPOMI/S5P aerosol layer height using EARLINET lidars

The purpose of this study is to investigate the ability of the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) to derive accurate geometrical features of lofted aerosol layers, selecting the Mediterranean Basin as the study area. Comparisons with ground-based correlative measurements constitute a key component in the validation of passive and active satellite aerosol products. For this purpose, we use ground-based observations from quality-controlled lidar stations reporting to the European Aerosol Research Lidar Network (EARLINET). An optimal methodology for validation purposes has been developed and applied using the EARLINET optical profiles and TROPOMI aerosol products, aiming at the in-depth evaluation of the TROPOMI aerosol layer height (ALH) product for the period 2018 to 2022 over the Mediterranean Basin. Seven EARLINET stations were chosen, taking into consideration their proximity to the sea, which provided 63 coincident aerosol cases for the satellite retrievals. In the following, we present the first validation results for the TROPOMI/S5P ALH using the optimized EARLINET lidar products employing the automated validation chain designed for this purpose. The quantitative validation at pixels over the selected EARLINET stations illustrates that the TROPOMI ALH product is consistent with the EARLINET lidar products, with a high correlation coefficient R=0.82 (R=0.51) and a mean bias of -0.51±0.77 km and -2.27±1.17 km over ocean and land, respectively. Overall, it appears that aerosol layer altitudes retrieved from TROPOMI are systematically lower than altitudes from the lidar retrievals. High-albedo scenes, as well as low-aerosol-load scenes, are the most challenging for the TROPOMI retrieval algorithm, and these results testify to the need to further investigate the underlying cause. This work provides a clear indication that the TROPOMI ALH product can under certain conditions achieve the required threshold accuracy and precision requirements of 1 km, especially when only ocean pixels are included in the comparison analysis. Furthermore, we describe and analyse three case studies in detail, one dust and two smoke episodes, in order to illustrate the strengths and limitations of the TROPOMI ALH product and demonstrate the presented validation methodology. The present analysis provides important additions to the existing validation studies that have been performed so far for the TROPOMI S5P ALH product, which were based only on satellite-to-satellite comparisons.</p

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA&apos;s TEMPO and ESA&apos;s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Quantifying the single-scattering albedo for the January 2017 Chile wildfires from simulations of the OMI absorbing aerosol index

The absorbing aerosol index (AAI) is a qualitative parameter directly calculated from satellite-measured reflectance. Its sensitivity to absorbing aerosols in combination with a long-term data record since 1978 makes it an important parameter for climate research. In this study, we attempt to quantify aerosol absorption by retrieving the single-scattering albedo (ω0) at 550 nm from the satellite-measured AAI. In the first part of this study, AAI sensitivity studies are presented exclusively for biomass-burning aerosols. Later on, we employ a radiative transfer model (DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument (OMI) in order to derive ω0 at 550 nm. Inputs for the radiative transfer calculations include satellite measurement geometry and surface conditions from OMI, aerosol optical thickness (τ) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and aerosol microphysical parameters from the AErosol RObotic NETwork (AERONET), respectively. This approach is applied to the Chile wildfires for the period from 26 to 30 January 2017, when the OMI-observed AAI of this event reached its peak. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses missed the evolution of the smoke plume over the research region; therefore the aerosol profile is parameterized. The simulated plume is at an altitude of 4.5-4.9 km, which is in good agreement with available CALIOP backscatter coefficient measurements. The data may contain pixels outside the plume, so an outlier detection criterion is applied. The results show that the AAI simulated by DISAMAR is consistent with satellite observations. The correlation coefficients fall into the range between 0.85 and 0.95. The retrieved mean ω0 at 550 nm for the entire plume over the research period from 26 to 30 January 2017 varies from 0.81 to 0.87, whereas the nearest AERONET station reported ω0 between 0.89 and 0.92. The difference in geolocation between the AERONET site and the plume, the assumption of homogeneous plume properties, the lack of the aerosol profile information and the uncertainties in the inputs for radiative transfer calculation are primarily responsible for this discrepancy in ω0.</p

Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning

Quantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based aerosol robotic network (AERONET), whereas it is still challenging to retrieve them from space. However, we found the AERONET AAOD has a relatively strong correlation with the satellite retrieved ultra-violet aerosol index (UVAI). Based on this, a numerical relation is built by a deep neural network (DNN) to predict global AAOD and SSA over land from the long-term UVAI record (2006-2019) provided by the ozone monitoring instrument (OMI) onboard Aura. The DNN predicted aerosol absorption is satisfying for samples with AOD at 550 nm larger than 0.1, and the DNN model performance is better for smaller absorbing aerosols (e.g., smoke) than larger ones (e.g., mineral dust). The comparison of the DNN predictions with AERONET shows a high correlation coefficient of 0.90 and a root mean square of 0.005 for the AAOD, and over 80% of samples are within the expected uncertainty of AERONET SSA (pm0.03).</p

Aerosol optical depth retrieval using ATSR-2 and AVHRR data during TARFOX

Satellite retrieved aerosol optical properties are compared to aircraft measurements for a case study during the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX). Two satellite instruments are used: the Along Track Scanning Radiometer 2 (ATSR-2) and the advanced very high resolution radiometer (AVHRR). The aerosol optical depth in the mid-visible (0.555 Jjm) retrieved from the ATSR-2 data agrees within 0.03 with colocated sunphotometer measurements. Also, the spectral behavior of the aerosol optical depth is retrieved accurately. Good correlation is found between aerosol optical depths for AVHRR channel 1 (0.64 Jjm) and sunphotometer derived values, but the satellite retrieved values are 0.05 to 0.15 lower. The Angstrom wavelength exponent is determined both from the ATSR-2 and the AVHRR data. The ATSR-2 derived Angstrom exponents are in good agreement with the values computed from the sunphotometer data. The Angstrom exponents determined from AVHRR data show very large variations. Both the ATSR-2 and the AVHRR aerosol optical depth images show a large gradient. Vertical profile data of temperature, relative humidity, and particle scattering indicate that this gradient is probably caused by changes in the dry aerosol properties, rather than a change in the relative humidity

The role of aerosol layer height in quantifying aerosol absorption from ultraviolet satellite observations

The purpose of this study is to demonstrate the role of aerosol layer height (ALH) in quantifying the single scattering albedo (SSA) from ultraviolet satellite observations for biomass burning aerosols. In the first experiment, we retrieve SSA by minimizing the near-ultraviolet (near-UV) absorbing aerosol index (UVAI) difference between observed values and those simulated by a radiative transfer model. With the recently released S-5P TROPOMI ALH product constraining forward simulations, a significant gap in the retrieved SSA (0.25) is found between radiative transfer simulations with spectral flat aerosols and those with strong spectrally dependent aerosols, implying that inappropriate assumptions regarding aerosol absorption spectral dependence may cause severe misinterpretations of the aerosol absorption. In the second part of this paper, we propose an alternative method to retrieve SSA based on a long-term record of co-located satellite and ground-based measurements using the support vector regression (SVR) approach. This empirical method is free from the uncertainties due to the imperfection of a priori assumptions on aerosol microphysics seen in the first experiment. We present the potential capabilities of SVR using several fire events that have occurred in recent years. For all cases, the difference between SVR-retrieved SSA and AERONET are generally within ±0:05, and over half of the samples are within ±0:03. The results are encouraging, although in the current phase the model tends to overestimate the SSA for relatively absorbing cases and fails to predict SSA for some extreme situations. The spatial contrast in SSA retrieved by radiative transfer simulations is significantly higher than that retrieved by SVR, and the latter better agrees with SSA from MERRA-2 reanalysis. In the future, more sophisticated feature selection procedures and kernel functions should be taken into consideration to improve the SVR model accuracy. Moreover, the high-resolution TROPOMI UVAI and co-located ALH products will guide us to more reliable training data sets and more powerful algorithms to quantify aerosol absorption from UVAI records.</p